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Registro Completo |
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
26/03/2008 |
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Data da última atualização: |
27/06/2018 |
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Autoria: |
SILVA, F. F. e; SÁFADI, T.; MUNIZ, J. A.; AQUINO, L. H. de; MOURÃO, G. B. |
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Afiliação: |
Fabyano Fonseca e Silva, Universidade Federal de Viçosa; Thelma Sáfadi, Universidade Federal de Lavras; Joel Augusto Muniz, Universidade Federal de Lavras; Luiz Henrique de Aquino, Universidade Federal de Lavras; Gerson Barreto Mourão, Escola Superior de Agricultura Luiz de Queiroz. |
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Título: |
Comparação bayesiana de modelos de previsão de diferenças esperadas nas progênies no melhoramento genético de gado Nelore. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 43, n. 1, p. 37-45, jan. 2008 |
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Idioma: |
Português |
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Conteúdo: |
O objetivo deste trabalho foi realizar uma análise bayesiana de modelos auto-regressivos de ordem p, AR(p), para dados em painel referentes às diferenças esperadas nas progênies (DEP) de touros da raça Nelore publicados de 2000 a 2006. Neste trabalho, adotou-se o modelo AR(2), indicado pela análise prévia da função de autocorrelação parcial. As comparações entre as prioris, realizadas por meio do Fator de Bayes e do Pseudo-Fator de Bayes, indicaram superioridade da priori independente t-Student multivariada ? Gama inversa em relação à priori hierárquica Normal multivariada ? Gama inversa e a priori de Jeffreys. Os resultados indicam a importância de se dividir os animais em grupos homogêneos de acordo com a acurácia. Constatou-se também que, em média, a eficiência de previsão dos valores de DEP para um ano futuro foi próxima de 80%. |
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Palavras-Chave: |
algoritmos MCMC; autoregressive model; dados em painel; MCMC algorithm; modelo auto-regressivo; panel data. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/38134/1/43n01a06.pdf
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Marc: |
LEADER 01623naa a2200241 a 4500
001 1123177
005 2018-06-27
008 2008 bl uuuu u00u1 u #d
100 1 $aSILVA, F. F. e
245 $aComparação bayesiana de modelos de previsão de diferenças esperadas nas progênies no melhoramento genético de gado Nelore.
260 $c2008
520 $aO objetivo deste trabalho foi realizar uma análise bayesiana de modelos auto-regressivos de ordem p, AR(p), para dados em painel referentes às diferenças esperadas nas progênies (DEP) de touros da raça Nelore publicados de 2000 a 2006. Neste trabalho, adotou-se o modelo AR(2), indicado pela análise prévia da função de autocorrelação parcial. As comparações entre as prioris, realizadas por meio do Fator de Bayes e do Pseudo-Fator de Bayes, indicaram superioridade da priori independente t-Student multivariada ? Gama inversa em relação à priori hierárquica Normal multivariada ? Gama inversa e a priori de Jeffreys. Os resultados indicam a importância de se dividir os animais em grupos homogêneos de acordo com a acurácia. Constatou-se também que, em média, a eficiência de previsão dos valores de DEP para um ano futuro foi próxima de 80%.
653 $aalgoritmos MCMC
653 $aautoregressive model
653 $adados em painel
653 $aMCMC algorithm
653 $amodelo auto-regressivo
653 $apanel data
700 1 $aSÁFADI, T.
700 1 $aMUNIZ, J. A.
700 1 $aAQUINO, L. H. de
700 1 $aMOURÃO, G. B.
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 43, n. 1, p. 37-45, jan. 2008
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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Registro Completo
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Biblioteca(s): |
Embrapa Cerrados. |
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Data corrente: |
14/12/2020 |
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Data da última atualização: |
14/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ALENCAR, A.; SHIMBO, J. Z.; LENTI, F.; MARQUES, C. B.; ZIMBRES, B.; ROSA, M.; ARRUDA, V.; CASTRO, I.; RIBEIRO, J. P. F. M.; VARELA, V.; ALENCAR, I.; PIONTEKOWSKI, V.; RIBEIRO, V.; BUSTAMANTE, M. M. C.; SANO, E. E.; BARROSO, M. |
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Afiliação: |
EDSON EYJI SANO, CPAC. |
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Título: |
Mapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 6, 2020. |
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ISSN: |
2072-4292 |
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Idioma: |
Português |
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Conteúdo: |
Widespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985?2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr?1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome. MenosWidespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985?2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr?1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other ... Mostrar Tudo |
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Thesagro: |
Cerrado; Desmatamento; Floresta; Pastagem. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/219146/1/SANO-MAPPING-THREE-DECADES-OF-CHANGES-IN-THE-BRAZILIAN-CERRADO-USING-GEE.pdf
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Marc: |
LEADER 02778naa a2200361 a 4500
001 2128057
005 2020-12-14
008 2020 bl uuuu u00u1 u #d
022 $a2072-4292
100 1 $aALENCAR, A.
245 $aMapping Three Decades of Changes in the Brazilian Savanna Native Vegetation Using Landsat Data Processed in the Google Earth Engine Platform.$h[electronic resource]
260 $c2020
520 $aWidespread in the subtropics and tropics of the Southern Hemisphere, savannas are highly heterogeneous and seasonal natural vegetation types, which makes change detection (natural vs. anthropogenic) a challenging task. The Brazilian Cerrado represents the largest savanna in South America, and the most threatened biome in Brazil owing to agricultural expansion. To assess the native Cerrado vegetation (NV) areas most susceptible to natural and anthropogenic change over time, we classified 33 years (1985?2017) of Landsat imagery available in the Google Earth Engine (GEE) platform. The classification strategy used combined empirical and statistical decision trees to generate reference maps for machine learning classification and a novel annual dataset of the predominant Cerrado NV types (forest, savanna, and grassland). We obtained annual NV maps with an average overall accuracy ranging from 87% (at level 1 NV classification) to 71% over the time series, distinguishing the three main NV types. This time series was then used to generate probability maps for each NV class. The native vegetation in the Cerrado biome declined at an average rate of 0.5% per year (748,687 ha yr?1), mostly affecting forests and savannas. From 1985 to 2017, 24.7 million hectares of NV were lost, and now only 55% of the NV original distribution remains. Of the remnant NV in 2017 (112.6 million hectares), 65% has been stable over the years, while 12% changed among NV types, and 23% was converted to other land uses but is now in some level of secondary NV. Our results were fundamental in indicating areas with higher rates of change in a long time series in the Brazilian Cerrado and to highlight the challenges of mapping distinct NV types in a highly seasonal and heterogeneous savanna biome.
650 $aCerrado
650 $aDesmatamento
650 $aFloresta
650 $aPastagem
700 1 $aSHIMBO, J. Z.
700 1 $aLENTI, F.
700 1 $aMARQUES, C. B.
700 1 $aZIMBRES, B.
700 1 $aROSA, M.
700 1 $aARRUDA, V.
700 1 $aCASTRO, I.
700 1 $aRIBEIRO, J. P. F. M.
700 1 $aVARELA, V.
700 1 $aALENCAR, I.
700 1 $aPIONTEKOWSKI, V.
700 1 $aRIBEIRO, V.
700 1 $aBUSTAMANTE, M. M. C.
700 1 $aSANO, E. E.
700 1 $aBARROSO, M.
773 $tRemote Sensing$gv. 12, n. 6, 2020.
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